Computational chemistry is a largely empirical field that makes predictions with substantial uncertainty. And yet the use of standard statistical methods to quantify this uncertainty is often absent from published reports. This article covers the basics of confidence interval estimation for molecular modeling using classical statistics. Alternate approaches such as non-parametric statistics and...

By applying atomic force microscope (AFM)-based force spectroscopy together with computational modeling in the form of molecular force-field simulations, we have determined quantitatively the actuation energetics of a synthetic motor-molecule. This multidisciplinary approach was performed on specifically designed, bistable, redox-controllable [2]rotaxanes to probe the steric and electrostatic i...

Simulations of molecular systems typically handle interactions within non-bonded pairs. Generating and updating a list of these pairs can be the most time-consuming part of energy calculations for large systems. Thus, efficient non-bonded list processing can speed up the energy calculations significantly. While the asymptotic complexity of current algorithms (namely O(N), where N is the number ...

The recent advances in genetic engineering, high powered computing and global optimization continue to stimulate interest in the area of molecular modeling and protein structure prediction. The goal of these eeorts is the ability to correctly predict native protein conformations and the binding interactions of macromolecules. These two problems currently dominate the eld of computational chemis...

In molecular shape analysis and in the analysis of various molecular functions used in modeling chemical reactions and biochemical interactions, it is often useful to generate transformations that interconvert various molecular models into one another. Some of these models are continua whereas some others have disconnected parts or even separated discrete points. The interconversion between suc...

Computational models of cells, tissues and organisms are necessary for increased understanding of biological systems. In particular, modeling approaches will be crucial for moving biology from a descriptive to a predictive science. Pharmaceutical companies identify molecular interventions that they predict will lead to therapies at the organism level, suggesting that computational biology can p...

The traditional approach to computational biophysics studies of molecular systems is brute force molecular dynamics simulations under the conditions of interest. The disadvantages of this approach are that the time and length scales that are accessible to computer simulations often do not reach biologically relevant scales. An alternative approach, which we call intuitive modeling, is hypothesi...

Molecular simulations and molecular docking are widely used tools to investigate ligand/target interactions in drug design. High-performance computing (HPC) is boosting both the accuracy predictive power of these approaches. With advent exascale computing, HPC may become standardly applied many design campaigns pharmacological applications. This review discusses how innovative algorithms hardwa...